Ultrasomics prediction for cytokeratin 19 expression in hepatocellular carcinoma: A multicenter study

作者：Linlin Zhang, Linlin Zhang, Qinghua Qi, Qian Li, Shanshan Ren, Shunhua Liu, Bing Mao, Xin Li, Yuejin Wu, Lanling Yang, Luwen Liu, Yaqiong Li, Shaobo Duan, Lianzhong Zhang, Lianzhong Zhang · 发表于：Frontiers in Oncology · 年份：2022 · DOI：10.3389/fonc.2022.994456 · 被引用次数：13 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、AI in cancer detection、Cholangiocarcinoma and Gallbladder Cancer Studies

Objective: The purpose of this study was to investigate the preoperative prediction of Cytokeratin (CK) 19 expression in patients with hepatocellular carcinoma (HCC) by machine learning-based ultrasomics. Methods: We retrospectively analyzed 214 patients with pathologically confirmed HCC who received CK19 immunohistochemical staining. Through random stratified sampling (ratio, 8:2), patients from institutions I and II were divided into training dataset (n = 143) and test dataset (n = 36), and patients from institution III served as external validation dataset (n = 35). All gray-scale ultrasound images were preprocessed, and then the regions of interest were then manually segmented by two sonographers. A total of 1409 ultrasomics features were extracted from the original and derived images. Next, the intraclass correlation coefficient, variance threshold, mutual information, and embedded method were applied to feature dimension reduction. Finally, the clinical model, ultrasonics model, and combined model were constructed by eXtreme Gradient Boosting algorithm. Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: A total of 12 ultrasomics signatures were used to construct the ultrasomics models. In addition, 21 clinical features were used to construct the clinical model, including gender, age, Child-Pugh classification, hepatitis B surface antigen/hepatitis C virus antibody (positive/neg...